Infrastructure As Code With SageMaker — In the past I’ve worked with SageMaker Deployment through Jupyter Notebooks and Python scripts. This is completely fine, but often times in the scope of a larger applications, you need to be able to define your SageMaker resources with the rest of your infrastructure in a central template. This brings…

Stop Waiting For Your Endpoints To Create — For frequent users of SageMaker Inference a common frustration is being able to debug endpoints quickly. Often times with SageMaker Endpoints you end up with a custom inference script that helps you control the pre and post processing of your model. Initially when I first started with SageMaker I would…

Guide On Deploying ML Models With Flask and Containerizing Your Work — Deploying ML models is an essential step in the ML Lifecycle that’s often overlooked by Data Scientists. Without model deployment/hosting, there is no usage of Machine Learning models in real-world applications. There’s a variety of ways to host your ML models and one of the simplest, yet most effective is…

Step by Step Guide — Amazon Elastic Container Registry (ECR) is a container image registry that we can use push Docker images to on AWS. Why use a Container Registry? It helps make it easy to manage your various images and separate projects. For example, when I first started working with Docker locally I didn’t…

Serverless made easier than ever — Traditionally in AWS applications you had an API Gateway fronting your Lambda function. The REST API created by API Gateway would serve as the endpoint to invoke to access your backend Lambda Function. While there’s nothing wrong with this pattern it added some extra grunt work. …

End-to-End Example Utilizing Rekognition Custom Labels for Image Classification — Outside of Amazon SageMaker, AWS contains a suite of AI/ML services that are tailored for AutoML. In cases that you need to inject ML into your applications and don’t have the theoretical experience or time to build your own models, this set of services proves to be extremely handy. Under…

Deploy TensorFlow models as REST endpoints — Training a Machine Learning (ML) model is only one step in the ML lifecycle. There’s no purpose to ML if you cannot get a response from your model. You must be able to host your trained model for inference. There’s a variety of hosting/deployment options that can be used for…

Generate large offline predictions with an Sklearn example — In my last article I talked about the latest SageMaker Inference option in Serverless Inference. An older, yet equally important option is SageMaker Batch Transform. Sometimes for our Machine Learning models we don’t necessarily need a persistent endpoint. We just have a large set of data and we want inference…

Exploring The Latest SageMaker Inference Option — I’ve been super excited to write this article. ML Inference is super interesting in itself. Add serverless to it and it becomes that much more interesting! When we talked about sServerless Inference before we had to look at potentially using services such as AWS Lambda.The problem with services such as…